The Science of Service Systems

The aim of this chapter is to identify the key dimensions of service systems and to describe how they interact in the process of value co-creation. The four key dimensions identified in the analysis are: customers; people (including employees and other stakeholders); information; and technology. The chapter also characterises the value-creation process in service systems as consisting of three related stages: value proposition; acceptance; and fulfilment. The main conclusion of the chapter is that the four key dimensions interact at all three stages in a network of relationships that co-create value through the integration of resources. In details, the interactions between the key dimensions shape two kinds of nets: (i) a social network; (ii) a technological network. These nets are the basis for a greater value-creating network aimed at increasing stakeholder value. Conclusions have practical implications for managers and theoretical implications for researchers.

This book’s theme is “The Science of Service Systems,” yet there is substantial question about whether the definition and nature of service systems have been articulated adequately. This paper examines definitions of service and service system that could frame or otherwise influence future developments in service science and could have implications for what should and should not be included within service science. It argues that the initial development of service science should use straightforward definitions that are understandable, useful, broadly applicable, and teachable. It proposes a definition of service system that is different from the definition proposed in this book’s Call for Chapters and in the 2008 White Paper produced by a service innovation symposium attended by many leaders in the effort to create service science. In comparison with that definition, this paper’s alternative definition is more understandable, useful, broadly applicable, and teachable.

Service science combines scientific, management, and engineering disciplines to improve the understanding of how service systems cooperate to create business value. Service systems are complex configurations of people, technologies, and resources that coexist in a common environment of service provisioning. While the general concepts of service science are understood and agreed upon, the representation of service systems using models is still in its infancy. In this chapter, we look at business processes and their role in properly representing service systems. We propose flexible process graphs, a high-level process modeling language, and extend it in order to specify service systems and their compositions within shared environments in a flexible way. The discussion in this chapter is the first step towards a formal description of service science environment, including service systems, networks, and whole ecology.

In this chapter, the notion of Smart Services is developed based on the concept of semantics and by borrowing ideas from existing planning techniques known from the AI domain. The motivation behind Smart Services is the creation of efficient and effective service systems self-adapting to a constantly changing environment. A major prerequisite for achieving this goal is a semantic description of the considered service system. Different standards and techniques evolved over the past years and can be applied to address this problem. Based on ideas borrowed from situational calculus, different planning techniques can then derive executable plans for achieving certain goals within the service system. Finally, an example from the Web Service domain is used in order to demonstrate the approach. Results show that this approach is promising to increase the level of automation for certain service systems and thereby creates a notion of Smart Services.

The evolving service ecologies show new ways of value co-creation through combinations of multiple service components which are described in service offerings. An open issue in such a large service ecology is how to efficiently coordinate and price service offerings. Service offerings provide different functionality and quality. Customers need to distinguish their preferences on different combinations of service attributes. In this chapter we address this issue of service offerings allocation and introduce a structure design approach, Market Engineering, as an appropriate method to design such mechanism. In order to apply this approach to service systems we introduce a formal model and a definition of service value networks. Examples exemplify our approach and we show one possible step towards implementing such a mechanism.

Over the past years service science has changed. Nowadays the object of research is highly professionalized complex service systems. For this area of research, service systems modeling provides concepts and formalized meta-models for describing service systems in a precise way. In this paper different aspects of service systems modeling are presented: (a) the specification of singular service components (component model), (b) the specification of the component’s resources (resource model), (c) the definition of interdependencies of service components relevant for configuration (product model) and (d) the temporal dependencies between service components necessary for defining process instances (process model). This paper offers a modeling-relevant definition of service systems, the theoretical foundation of the meta-model (based on a wide literature research) as well as the concepts and terms, necessary for modeling service systems. Finally, the advantages and limitations of service system modeling are discussed.

In this chapter, we report the design of an ontology (called onto-ServSys) for the relevant service system construct used in the Service Science, Management and Engineering (SSME) arena. These conceptual artifacts have been used to establish a non-ambiguous and common set of basic constructs for supporting automated reasoning on a domain of interest in intelligent systems. While this reported ontology is initial, and still conceptual, we claim it is useful to advance on an integrated view of service systems, which at present, given the multiple and disparate literatures, is fragmented and disperse. Such integration is realized through a Systems Approach, and its design is guided by a conceptual design research method.

Service science strives to bring together many disciplines (including computer science, cognitive science, economics, organizational behavior, human resources management, marketing, operations research, and others) in an attempt to study service systems in the following ways: understanding what service systems are and how they evolve; studying how to invest in order to improve management practices in service systems; determining how to create new technologies that increase the scaling of service systems; and, establishing a basis for assessing and relating relevant interdisciplinary knowledge within this emerging field. Academic and industry researchers from separate and currently mostly isolated disciplines are each approaching the field of service science from their own perspectives. This chapter presents a framework that technology researchers can use to understand and articulate how their research relates to the field of service science. The result will better enable technology researchers to relate to and engage with other researchers in the interdisciplinary field of service science.

Service Dominant Logic (SDL), a contemporary view of services as a foundation of all economic exchange, takes a very high level perspective of provider–customer interaction. The role of the customers in value creation is emphasized but their role in the creation of the value proposition choice sets is not explicitly considered. From another perspective, the notion of value co-creation addressed in existing Service Science studies often assumes the value proposition to be static – i.e., proposition/acceptance happens before the start of service and is not visited again during the service. This paper attempts to connect the macro view of SDL to the system view of Service Science in creating a framework of Service Value Network (SVN) that accounts for both provider and customer driven value co-creation.

The integration of customers (often as an additional vendor) into global service delivery with industrialized processes is a challenge: When running software projects for such settings we see with all customers the practical need to harmonize governance – over projects inside an enterprise for a particular customer, over vendors for a particular project, over projects of a particular vendor for different customers. This chapter discusses this urgent need to innovate interactions – while leveraging the service system grammar to organize governance in a multi-sourcing environment with global service delivery. We focus on streamlining the architecture and the design to allow simpler i.e. simple governance in a global service delivery network of multiple service systems. With service perspectives derived from the service system grammar the creation of service systems empowers design and architecture of the service system at the right point in time with the right level of detail. The purpose of this chapter is to describe this shift in approaching service governance.

The evolution of service science or Service Science, Management and Engineering – SSME, provides us with a platform to critically review the area of service design. The drivers for this include the lack of cross-disciplinary writing on service design, the limitations of the treatment of service design as an extension of product design and the dominance of B2C and neglect of B2B design. Three perspectives are used: service delivery systems, service architecture including modularity and platforms, and the service supply chain/network. Empirical examples are provided and a service modularity function is developed. It is argued that an important role of SSME is to be able to link the operationally based service architectures and resulting design methods and information system (IS) architectures, and that there is a need to develop a combined view of the physical, organisational, and IS architectures of services.

Several authors have claimed that there is an increasing demand for multidisciplinary and cross-disciplinary work in service science, management and innovation. Especially in the service area there is a need to break down the barriers between disciplines. At the same time here is evidence that joint multidisciplinary work by authors in academic journals is only increasing marginally. Another weakness is the lack of real academia–company interaction. Service sector companies have accumulated significant experiential knowledge base and tacit insight from their engagements with many real life applications and successes, but these have often not been studied by academicians for abstraction and understanding of principles. This calls for more study as well. As academia cannot bridge the gap alone with their traditional curricula, there is a most important role for new learning approaches incorporating cross disciplinary and academia–company learning at the group level. In this case, bringing the group approach to learning means contributions from a wide area of disciplines and participation from academia as well as from companies. Problem based learning (PBL) seems to be an approach that provides the necessary structure for systematic goal oriented collaboration while encouraging new paradigms to emerge.

This article focuses on the way in which engineering, as a discipline, can most effectively interact with the services sectors generally, and with service science in particular. This perspective is proposed in order to balance the relatively limited recognition of both engineering as a contributor to service science and also services as an application area for engineering developments. The article is structured as a response to a series of questions relating to the current and potential future role engineering can best support the evolving area of Service Science, Management and Engineering

For professionals at the beginning of the twenty-first century, much of the conventional wisdom on business management and engineering is founded in the twentieth century industrial/manufacturing paradigm. In developed economies, however, the service sector now dominates the manufacturing sector, just as manufacturing prevailed over the agricultural sector after the industrial revolution. This chapter proposes the development of a body of knowledge on services systems, based on foundations in the systems sciences. The approach includes the design of the systems of inquiry, acknowledging that the body of knowledge on twenty-first century service systems is relatively nascent. A program of action science is proposed, with an emphasis on multiple realities and knowledge development through dialectic. The outcome pursued is an increased number of T-shaped people with depth and breadth in service systems, in communities of inquiry of researchers and practitioners.

Service customization is an important new opportunity for firms in their pursuit of better outcomes in their service provision process. Much like the recent expansion of product customization in goods markets, service customization has been expanding recently due to a variety of factors, such as technological development allowing for improved service delivery and better communication with customers as well as a growing acceptance amongst customers of the customization process. Much of the previous research on services in this area has viewed service variability as a negative that should be limited. However, customer variability in product needs provides a similar opportunity to deliver more value for consumers through specialization of services. In fact, the co-creation of value in services makes the development of effective mass customization systems in the area of services exceptionally important. This chapter will examine service mass customization and the design of systems for service customization. Starting with a review of extant work, the chapter will develop a framework for service customization and service design, specifically, focusing on the concept of customer variability and how this concept can be used to extract greater value from the transaction between the customer and the firm. Based on the framework, the chapter will then identify important directions for further research from both practice and academic viewpoints.

While there is a rapid growth in the number of researchers and practitioners joining the service science community, this community has not yet settled on precise answers to two fundamental questions: “What is service?” and “Where is the science (in service science)?” This chapter examines possible answers to these two fundamental questions from multiple disciplinary perspectives, and proposes the Abstract Entity-Interaction-Outcome Universals (AEIOU) theory to frame the science of service systems.